Abstract
Most machine learning-based methods predict outcomes rather than understanding causality. Machine learning methods have been proved to be efficient in finding correlations in data, but unskilful to determine causation. This issue severely limits the applicability of machine learning methods to infer the causal relationships between the entities of a biological network, and more in general of any dynamical system, such as medical intervention strategies and clinical outcomes system, that is representable as a network. From the perspective of those who want to use the results of network inference not only to understand the mechanisms underlying the dynamics, but also to understand how the network reacts to external stimuli (e. g. environmental factors, therapeutic treatments), tools that can understand the causal relationships between data are highly demanded. Given the increasing popularity of machine learning techniques in computational biology and the recent literature proposing the use of machine learning techniques for the inference of biological networks, we would like to present the challenges that mathematics and computer science research faces in generalising machine learning to an approach capable of understanding causal relationships, and the prospects that achieving this will open up for the medical application domains of systems biology, the main paradigm of which is precisely network biology at any physical scale.
Highlights
The availability of big data, the use of machine learning techniques to process them, and the opportunity to access and/or perform high-performance computing are becoming of crucial importance for biology (Xu and Jackson, 2019), medicine and healthcare (Bates et al, 2020; Prosperi et al, 2020)
Machine learning can perform many tasks, such as classification, regression, transcription, machine translation, anomaly detection, imputation of missing values, de-noising, probability density estimation (Goodfellow et al, 2016), but causal inference is still a challenge for it, because of its inability to implement a generalization from one problem to the rather than a generalization from a data point to the
To the best of our knowledge, their evolution as a function of causal inference has not yet been developed, in the literature we find some works in which support vector machine (SVM) are used in combination with other techniques in order to infer the structure of gene regulatory networks
Summary
The availability of big data, the use of (deep) machine learning techniques to process them, and the opportunity to access and/or perform high-performance computing are becoming of crucial importance for biology (Xu and Jackson, 2019), medicine and healthcare (Bates et al, 2020; Prosperi et al, 2020). Datadriven prediction models - often implemented by machine learning algorithms - even assuming they are derived from an experiment in which several bias were minimised, should be used with great caution and their results should be subjected to critical review before interpretation These methods are widely used to draw cause-effect relationships, attention must be paid to the fact that neither their parameters nor their predictions necessarily have a causal interpretation (Prosperi et al, 2020). The perspectives presented and discussed in this paper are conceived with particular reference to biological networks of any scale (molecular, cellular, ecosystem), but remain valid in other research areas where the problem of inferring causal relationships in a dynamical system is posed
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